A comparative study on vectorization methods for non-functional requirements classification
A comparative study on vectorization methods for non-functional requirements classification
复制标题
非功能需求分类向量化方法比较研究
DOI：
10.1016/j.infsof.2022.106991
复制
发表时间：
2022
影响因子：
3.9
通讯作者：
Amasaki Sousuke
中科院分区：
文献类型：
--
作者：
Leelaprute Pattara;Amasaki Sousuke
上下文在早期阶段识别非功能需求(NFR)及其类别对于分析人员设计软件系统和识别约束至关重要。为了降低劳动密集型任务的成本，已经研究了非功能需求的自动分类方法。我们之前的研究集中在将自然语言编写的需求转换为用于分类的数字向量的向量化方法之间的差异。目的考察不同的矢量化方法是否会导致非特征向量机及其类别在扩展设置下的分类性能存在差异。给出了9种矢量化方法，包括预训练数据矢量化方法和4种监督分类方法。通过AUC和Scott-Knott ESD测试对性能进行评估。结果一些先进的方法与一些分类器相结合，可以获得比传统方法更好的性能。对于某些类别，使用预先训练的数据是有用的。结论考虑使用向量化方法和分类器的一些组合来对非功能需求类别进行分类是有益的。
ContextIdentifying non-functional requirements (NFRs) and their categories at the early phase is crucial for analysts to design software systems and recognize constraints. Automatic non-functional requirements classification methods have been studied for reducing the costs of that labor-intensive task. Our previous study focused on the differences among vectorization methods that converted requirements written in natural language into numerical vectors for classification. It had some limitations regarding the number of datasets used, the types of vectorization methods supporting pre-trained data, and the performance evaluation procedure.ObjectiveTo examine whether different vectorization methods lead to differences in the classification performance of NFRs and their categories with extended settings.MethodsComparative experiments were conducted with five open data. Nine vectorization methods, including ones with pre-trained data and four supervised classification methods, were supplied. Performance was evaluated with AUC and Scott-Knott ESD test.ResultsSome advanced methods could achieve better performance than traditional ones when combined with some classifiers. The use of pre-trained data was useful for some categories.ConclusionIt is beneficial to consider using some combinations of vectorization methods and classifiers for classifying non-functional requirements categories.